Revolutionizing dealmaking: insights from the AI bootcamp

Generative AI is transforming corporate dealmaking, and the insights from a recent AI bootcamp reveal exactly how M&A professionals are navigating this shift. Drawing from practitioners at Applied Materials, Cisco, Intuit, eBay, Broadcom, and Lam Research, this article distills the essential trends, practical applications, and strategic considerations shaping the future of M&A.

Where AI is making inroads today

Corporate development teams are playing catch-up compared to other functions, but the momentum is building. Product development, customer service, and engineering departments have led the charge in AI adoption, while corporate development is now accelerating its exploration of these tools.

The most common applications in dealmaking currently focus on:

• Drafting and negotiating letters of intent
• Creating M&A agreements and stock purchase agreements
• Streamlining routine legal documentation
• Automating standard due diligence tasks

Internal AI teams have emerged as crucial gatekeepers, vetting new technologies and ensuring responsible implementation. The driving force behind adoption remains clear: boosting efficiency and finding smarter ways to execute deals.

The economics of AI in M&A

A significant finding from the bootcamp challenges the notion that AI will replace outside counsel entirely. Instead, the consensus points to a more nuanced reality: generative AI will reduce spending on external lawyers by enabling companies to handle routine tasks in-house.

Companies anticipate shaving 10–15% off standard diligence costs, freeing up budget for more strategic legal advice. This shift reflects a broader trend toward selective automation that enhances rather than eliminates human expertise.

Current implementation approaches

When bootcamp attendees were polled, most reported their companies are officially using generative AI, though typically in controlled, specific ways. Common implementations include:

• Internal, sanitized versions of ChatGPT
• Protected Microsoft Copilot setups
• Custom large language models built on historical deal data

This cautious approach prioritizes control and security, reflecting the sensitive nature of M&A data. One Applied Materials representative described the challenge of building proprietary models as finding a “peanut butter solution” versatile enough for diverse historical data without being overly simplistic.

Notebook LM: a standout tool

Among the tools discussed, Notebook LM generated particular enthusiasm. This platform allows users to upload up to 300 documents on a specific topic, then uses AI to extract insights while treating the uploaded data as the source of truth.

An Intuit representative highlighted using Notebook LM for negotiating LOIs, analyzing typical escrow percentages by feeding it past M&A deals. This approach significantly reduces the risk of AI hallucinations since the tool only references provided documents.

Key features that resonated with practitioners include:

• Intuitive interface requiring minimal training
• Audio summaries that function like podcasts about your documents
• Executive summaries, timelines, mind maps, and FAQs
• Collaboration capabilities for internal and external sharing
• Natural language processing that understands and condenses information

The broader tool landscape

Beyond Notebook LM, companies are deploying a mix of general and specialized tools.

Custom solutions:

• eBay’s Hub GPT for searching internal and external information

Legal tech platforms:

• GCI Insight for redlining agreements
• Insight for legal research and analysis

This variety reflects different maturity levels across organizations. Many companies started with AI in product development or customer service before bringing it into corporate development and legal functions.

Governance and training: the foundation of success

Responsible AI programs emerged as a constant theme throughout the bootcamp. Companies recognize that strong governance and thorough vetting are essential for any AI tool, given the potential downsides of inadequate oversight.

Training programs vary in scope but are becoming increasingly comprehensive:

• Dedicated AI teams with ambassadors to spread knowledge
• Mandatory AI training for specific roles
• Continuous education on effective and responsible tool usage

This focus on skilling up the workforce ensures that AI adoption delivers genuine value rather than creating new problems.

AI across the deal lifecycle

Deal flow and pipeline management

Generative AI sees less current usage in the early stages of dealmaking compared to due diligence or integration. This phase remains heavily relationship-based, driven by corporate development expertise and established networks.

However, interest is growing in using AI research tools to supplement traditional methods. Potential applications include:

• Analyzing unstructured data from social media, industry reports, and pitch decks
• Ranking startups and scoring targets on financial, IP, and cultural factors
• Exploring adjacent markets to identify non-obvious targets

Practitioners caution that accuracy can be inconsistent at this stage. Human expertise and fundamental market understanding remain critical for validating AI-generated insights.

Due diligence and closing: the primary battleground

Due diligence emerged as the primary focus area for AI adoption. The transformative potential centers on revolutionizing the Q&A process by treating the data room itself as a private large language model.

Specific applications generating real value include:

• Quickly summarizing financial statements and complex IP filings
• Analyzing contracts to flag key risks and unusual clauses
• Highlighting compliance issues and litigation concerns
• Spotting market trends and competitive threats buried in documentation

eBay provided a compelling case study, reporting 98% accuracy when using their diligence data room as an LLM to answer thousands of standard questions. This approach saves hundreds of hours and reduces the burden on target companies by eliminating redundant information requests.

Companies are also demanding greater transparency from outside law firms about their AI tool usage. Clients want to know whether their advisers are leveraging platforms like Kira or Harvey to deliver efficiency gains.

Internal debates around data security and responsible AI use continue to slow some implementations, particularly given concerns about IP leaks and unauthorized data sharing.

Post-acquisition integration

Integration represents another area ripe for AI enhancement, though it currently lacks the enterprise-level tools available for legal applications.

Common challenges that AI could address include:

• Improving the handoff from diligence to integration
• Mapping organizational structures and tech stacks to identify overlaps
• Analyzing employee sentiment from internal communications
• Monitoring KPIs automatically to track synergy realization
• Ensuring compliance with earnouts and regulatory filings

The ultimate goal is linking achieved synergies back to the original deal rationale and ensuring this translates into how the combined company operates.

Overcoming adoption challenges

Several barriers continue to slow AI adoption in dealmaking.

Technology infrastructure: Companies need better integrated enterprise AI platforms with strong IT backing, not just disconnected point solutions.

Cultural resistance: Adoption speed varies significantly based on organizational culture and risk tolerance.

Problem-solution fit: Successful adoption requires solving real problems for people, making them feel the technology genuinely helps them do their jobs better.

Change management: Building empathy and understanding why people might be hesitant about AI is essential for sustainable adoption.

The road ahead

Despite these challenges, optimism pervades the M&A community. Practitioners anticipate significant progress over the next few quarters as tools become more sophisticated, user-friendly, and targeted to specific dealmaking needs.

The end goal remains consistent: use AI strategically to streamline the entire deal process, achieve major efficiency gains, and ultimately unlock more value from M&A activity.

Key takeaways

• Generative AI is making genuine inroads in corporate dealmaking, with due diligence as the primary beachhead.
• Adoption remains uneven as companies grapple with data security and internal support structures.
• AI should augment—not replace—human expertise.
• Governance, training, and transparency are essential for success.
• Change management must be built into every AI strategy.

As AI continues evolving at breakneck speed, the long-term implications for corporate development, legal, and other M&A functions remain profound. The practitioners leading this transformation aren't waiting for perfect solutions. They're experimenting, learning, and building the future of M&A one use case at a time.